code: def learn(self, obs, act, reward, next_obs, terminal):
      self.learn_it += 1
      feed = {
          "obs": obs,
          "act": act,
          "reward": reward,
          "next_obs": next_obs,
          "terminal": terminal,
      }
      critic_cost = self.fluid_executor.run(
          self.critic_learn_program,
          feed=feed,
          fetch_list=[self.critic_cost],
      )[0]
  
      actor_cost = None
      if self.learn_it % self.policy_freq == 0:
          actor_cost = self.fluid_executor.run(
              self.actor_learn_program,
              feed={"obs": obs},
              fetch_list=[self.actor_cost],
          )[0]
          self.alg.sync_target()
      return actor_cost, critic_cost
  
file_path: PARL/benchmark/fluid/TD3/mujoco_agent.py
imports: import numpy
  import parl
  from parl import layers
  from paddle import fluid
label_desc: Use image node_id 4 for calling the MujocoAgent obj's underlying member method code with example usage: obj.learn(obs, act, reward, next_obs, terminal) and returns: actor_cost,  critic_cost